IEEE 802.11az Indoor Positioning with mmWave

Last years we have witnessed the uprising of location based applications, which depend on the devices ability to accurately obtain their position. IEEE 802.11, foretelling the need for such applications, started the IEEE 802.11az work on Next Generation Positioning. Although this standard provides positioning enhancements for sub-6GHz and mmWave bands, high accuracy in the order of centimeters can only be obtained in the latter band, thanks to the beamforming information available at mmWave operation. This work presents a detailed analysis on the new techniques provided by IEEE 802.11az for enhanced secured positioning in the mmWave band, assessing them through experimentation.Comment: 8 pages, 6 figures, magazine submissio

Indoor vehicles geolocalization using LoRaWAN

[EN] One of the main drawbacks of Global Navigation Satellite Sytems (GNSS) is that they do not work indoors. When inside, there is often no direct line from the satellite signals to the device and the ultra high frequency (UHF) used is blocked by thick, solid materials such as brick, metal, stone or wood. In this paper, we describe a solution based on the Long Range Wide Area Network (LoRaWAN) technology to geolocalise vehicles indoors. Through estimation of the behaviour of a LoRaWAN channel and using trilateration, the localisation of a vehicle can be obtained within a 20¿30 m range. Indoor geolocation for Intelligent Transporation Systems (ITS) can be used to locate vehicles of any type in underground parkings, keep a platoon of trucks in formation or create geo-fences, that is, sending an alert if an object moves outside a defined area, like a bicycle being stolen. Routing of heavy vehicles within an industrial setting is another possibility.This work was partially supported by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00.Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Hernández-Orallo, E. (2019). Indoor vehicles geolocalization using LoRaWAN. Future Internet. 11(6):1-15. https://doi.org/10.3390/fi11060124S11511

Location Based Services is tracking YOU: a Netnographic perspective on challenges and opportunities from UK consumers

Location Based Services (LBS) and electronic lifestyles (e-lifestyles) represent emergent new areas with approaches (e.g. applications [apps] and electronic activities [e-activities]) billed to change customer experiences and responses (Zhou 2012). LBS is one of the IoT megatrends requiring tracking of consumers where fine grained user information is gathered when either requesting or receiving tailored location services. Other megatrend examples include Cyborgs and Artificial Intelligence applications. As such key objectives of this study are to, a) explore consumer experiences with location-based services, b) examine how respondent perceptions (value and risk) influence response to LBS and c) uncover how context may influence individual consumer response patterns. A mono method qualitative design was adopted and 85 threads from 4 websites were sampled. Piloting of relevant sites was conducted over a period of three weeks resulting in 4 websites that contained rich threads on LBS. Despite LBS being in infancy and muted lack of awareness (see Zhou, 2012) results emerged in our study pointing to rich consumer experiences with location services. Nonetheless, there was indifference in the absence of opt in and opt out options were concerns over perceived risk (e.g. data breaches, selling of data and the covert nature of services, spam: trust transfer process. As location-based services continue to increase in sophistication, there is need for more theoretical models that offer good explanatory potentialto understand consumer response further. Our research develops further research by seminal authors providing richer insights into specific privacy concerns when using location services

An inside vs. outside classification system for Wi-Fi IoT devices

We are entering an era in which Smart Devices are increasingly integrated into our daily lives. Everyday objects are gaining computational power to interact with their environments and communicate with each other and the world via the Internet. While the integration of such devices offers many potential benefits to their users, it also gives rise to a unique set of challenges. One of those challenges is to detect whether a device belongs to one’s own ecosystem, or to a neighbor – or represents an unexpected adversary. An important part of determining whether a device is friend or adversary is to detect whether a device’s location is within the physical boundaries of one’s space (e.g. office, classroom, home). In this thesis we propose a system that is able to decide with 82% accuracy whether the location of an IoT device is inside or outside of a defined space based on a small number of transmitted Wi- Fi frames. The classification is achieved by leveraging a machine-learning classifier trained and tested on RSSI data of Wi-Fi transmissions recorded by three or more observers. In an initialization phase the classifier is trained by the user on Wi-Fi transmissions of a variety of locations, inside (and outside). The system can be built with off-the-shelf Wi-Fi observing devices that do not require any special hardware modifications. With the exception of the training period, the system can accurately classify the indoor/outdoor state of target devices without any cooperation from the user or from the target devices

MoodFoam:an atmospheric evaluation of multi-spaces

Abstract. Understanding spatial experience is a multi-faceted problem which requires using tools from various fields. Spaces are rarely evaluated after they are built, and the spaces might not be used the way they were intended. This can lead to a lacking knowledge on how the spaces work or how they could be improved. This thesis aims to tackle this challenge by developing a methodology to gather data from a multi-space. To achieve this, a MoodFoam web application was developed for user surveys, and environmental sensor data was gathered from Tellus, a multi-space in the University of Oulu. The theoretical background of this thesis utilizes understanding from the fields of architecture, organization theory, philosophy, and psychology. The subjective and contextual user data and the objective sensor data was gathered successfully during the study’s two-week data gathering period. The data was analyzed and visualized with statistical programming language R to highlight various aspects of the spatial experience in Tellus. As a result, the methodology was able to produce subjective data with a time-space information allowing for a broader understanding of the multi-space and its users. The results suggest that even close-by spaces can have different atmospheres. Furthermore, the results found differences in the subjective experiences in the researched spaces, in terms of smell, temperature, and sound. The presented methodology can be further used in various contexts to improve its explicative capabilities.MoodFoam : monitilojen ilmapiirievaluaatio. Tiivistelmä. Tilakokemuksen ymmärtäminen on monitahoinen ongelma, joka vaatii eri alojen menetelmien hyödyntämistä. Tiloja arvioidaan harvoin niiden rakentamisen jälkeen, eikä niitä välttämättä käytetä niille tarkoitetulla tavalla. Tämä voi johtaa puutteelliseen ymmärrykseen tilojen toiminnasta tai siitä, miten niitä voisi kehittää. Tämä diplomityö pyrkii ratkaisemaan ongelman kehittämällä metodologia monitilan datankeruuseen. Tätä varten kehitettiin MoodFoam-internetsovellus käyttäjäkyselyille ja sensoridataa kerättiin Telluksesta, monitilasta Oulun yliopistolla. Diplomityön teoreettinen tausta hyödyntää arkkitehtuurin, organisaatioteorian, filosofian ja psykologian tarjoamaa ymmärrystä. Subjektiivinen ja kontekstuaalinen käyttäjädata sekä objektiivinen sensoridata kerättiin onnistuneesti diplomityön kahden viikon datankeruun aikajaksolta. Data analysoitiin ja visualisoitiin tilastollisella R-ohjelmointikielellä Telluksen moninaisten tilakokemusten korostamiseksi. Diplomityön tuloksena metodologia onnistui tuottamaan subjektiivista aika–paikka-tietoa mahdollistaen laajemman ymmärryksen monitiloista ja sen käyttäjistä. Tulokset viittaavat siihen, että lähekkäisilläkin tiloilla voi olla oma ilmapiiri. Lisäksi tuloksista löytyi eroja tutkittujen tilojen subjektiivisissa kokemuksissa hajun, lämpötilan ja äänen suhteen. Esiteltyä metodologiaa voidaan edelleen hyödyntää lukuisissa konteksteissa tehokkaamman kuvaavuuden kehittämiseksi

Research challenges in Measurement for Internet of Things systems

In this paper, an overview of the research challenges in measurements for the design of Internet of Things (IoT) systems is proposed. To this end, a general architecture of an IoT system is presented, which is specialized according to two key requirements: the power supply capabilities of the infrastructure and the time delay constraints of the application. Guidelines for the design of an IoT system are summarized, and the measurement needs are highlighted. A review of the research contributions is given concerning three main measurement topics: (i) energy-aware data acquisition systems, (ii) localization of mobile IoT nodes, and (iii) precise synchronization protocols

Patterns and Pattern Languages for Mobile Augmented Reality

Mixed Reality is a relatively new field in computer science which uses technology as a medium to provide modified or enhanced views of reality or to virtually generate a new reality. Augmented Reality is a branch of Mixed Reality which blends the real-world as viewed through a computer interface with virtual objects generated by a computer. The 21st century commodification of mobile devices with multi-core Central Processing Units, Graphics Processing Units, high definition displays and multiple sensors controlled by capable Operating Systems such as Android and iOS means that Mobile Augmented Reality applications have become increasingly feasible. Mobile Augmented Reality is a multi-disciplinary field requiring a synthesis of many technologies such as computer graphics, computer vision, machine learning and mobile device programming while also requiring theoretical knowledge of diverse fields such as Linear Algebra, Projective and Differential Geometry, Probability and Optimisation. This multi-disciplinary nature has led to a fragmentation of knowledge into various specialisations, making it difficult to integrate different solution components into a coherent architecture. Software design patterns provide a solution space of tried and tested best practices for a specified problem within a given context. The solution space is non-prescriptive and is described in terms of relationships between roles that can be assigned to software components. Architectural patterns are used to specify high level designs of complete systems, as opposed to domain or tactical level patterns that address specific lower level problem areas. Pattern Languages comprise multiple software patterns combining in multiple possible sequences to form a language with the individual patterns forming the language vocabulary while the valid sequences through the patterns define the grammar. Pattern Languages provide flexible generalised solutions within a particular domain that can be customised to solve problems of differing characteristics and levels of iii complexity within the domain. The specification of one or more Pattern Languages tailored to the Mobile Augmented Reality domain can therefore provide a generalised guide for the design and architecture of Mobile Augmented Reality applications from an architectural level down to the ”nuts-and-bolts” implementation level. While there is a large body of research into the technical specialisations pertaining to Mobile Augmented Reality, there is a dearth of up-to-date literature covering Mobile Augmented Reality design. This thesis fills this vacuum by: 1. Providing architectural patterns that provide the spine on which the design of Mobile Augmented Reality artefacts can be based; 2. Documenting existing patterns within the context of Mobile Augmented Reality; 3. Identifying new patterns specific to Mobile Augmented Reality; and 4. Combining the patterns into Pattern Languages for Detection & Tracking, Rendering & Interaction and Data Access for Mobile Augmented Reality. The resulting Pattern Languages support design at multiple levels of complexity from an object-oriented framework down to specific one-off Augmented Reality applications. The practical contribution of this thesis is the specification of architectural patterns and Pattern Language that provide a unified design approach for both the overall architecture and the detailed design of Mobile Augmented Reality artefacts. The theoretical contribution is a design theory for Mobile Augmented Reality gleaned from the extraction of patterns and creation of a pattern language or languages